四川轻化工大学学报（自然科学版）2026，Vol.39Issue(2)：49-57,9.DOI:10.11863/j.suse.2026.02.05
基于CLD-YOLO的高粱病虫害识别方法
Sorghum Pest and Disease Identification Method Based on CLD-YOLO
摘要
Abstract
To address the challenges of sorghum pest and disease detection in natural environments,we propose a sorghum pest and disease detection model,CLD-YOLO,based on an improved YOLOv8n.First,the ODConv structure is introduced into the backbone network to replace traditional convolutions.This modification reduces background noise interference and enhances the model's focus on sorghum leaf features,thereby improving detection accuracy.Additionally,the LSKA attention mechanism is incorporated after the SPPF module in the backbone network,effectively combining the strengths of convolution and self-attention to highlight the detailed features of diseases and pests,further enhancing detection precision.Finally,the DySample dynamic upsampling technique is applied in the neck network to enable more precise localization and identification of sorghum pest and disease-affected areas,significantly boosting detection performance.Experimental results show that CLD-YOLO achieves a mAP0.5 of 90.9%and an accuracy of 90.5%in sorghum pest and disease image recognition tasks,with a computational load of only 7.3 GFLOPs.Compared to YOLOv8n,mAP0.5 improved by 3.8 percentage points,accuracy by 5.1 percentage points,and computational load decreased by 9.9%.The improved model offers higher recognition accuracy and more precise target localization,making it suitable for accurate detection of sorghum pests and diseases in natural environments.It provides a theoretical basis for the intelligent recognition of sorghum pests and diseases.关键词
高粱病虫害/目标检测/注意力机制/动态上采样/YOLOv8nKey words
sorghum pests and diseases/target detection/attention mechanism/dynamic upsampling/YOLOv8n分类
信息技术与安全科学引用本文复制引用
任方俊,华才健,陈玉章..基于CLD-YOLO的高粱病虫害识别方法[J].四川轻化工大学学报（自然科学版）,2026,39(2):49-57,9.基金项目
国家自然科学基金面上项目(42471437) （42471437）
